---
title: "pranali-flexdashboard"
author: "Pranali Angne"
date: "5/21/2021"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---

```{r libraries, echo=FALSE}
library(flexdashboard)
library(tidyverse)
library(naniar)
library(ggplot2)
library(kableExtra)
library(plotly)
```

```{r}
# read tidy data
fatalcrashes <- read.csv(here::here("data/cleaned_fatalcrashes.csv"))
fatalities <- read.csv(here::here("data/cleaned_fatalities.csv"))
```


Question 1 {data-icon="fa-car-crash"}
====================

### Table 3.1: Australian road deaths database and fatal crashes, by age and gender

```{r setting-data}

accidents0 <- fatalities %>% 
  filter(!is.na(Gender)) %>% 
  select( "Gender", "Age.Group") %>% 
  group_by( Age.Group) %>% 
  count(Gender, Age.Group) %>% 
  rename(`Total no. of accidents` = n)
```


```{r Graph1}
graph1 <- ggplot(data = accidents0, aes(x = Age.Group,
                             y = `Total no. of accidents`, 
                             fill = Gender)) +
  geom_bar(stat="identity", position = "dodge") +
  scale_fill_brewer(palette = "Pastel2")+
  theme_classic() +
  xlab("Age Group") +
  ylab("Total number of Accidents") 


graph1
``` 

### Analysis 

+ The Australian Road Deaths Database provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities.


+ Road deaths from recent months are preliminary and the series is subject to revision.


+ The above graph explains, what demographic has a higher rate of traffic accidents considering **Age.Group** and **Gender** as a factor.


+ The age group between **40 to 64** in both the genders, had the maximum risk of fatal crash involvements compared to the rest of the age groups. 


+ **Male** drivers compared to **Female** for the same age group have the highest rate of road accidents i.e. more than **3000** fatal crashes. Hence, Male drivers between age group **40 t0 64** are more susceptible to the fatal crash involvement.    
 



Question 2 {data-icon="fa-car-crash"}
====================

### Graph 3.2: Road user is more prone to accident over the Year

```{r, setting-data-roaduser}
accidents1 <- fatalities %>% 
  mutate(Year = as.factor(Year)) %>% 
  select("Year", "Road.User") %>% 
  filter(Road.User %in% c("Driver", "Motorcycle pillion passenger", "Motorcycle rider", "Passenger", "Pedal cyclist", "Pedestrian")) %>% 
  group_by(Road.User, Year) %>% 
  count(Road.User) %>% 
  rename(`Accident Total count` = n)

```


```{r Graph2, fig.width=15, fig.height=6}
graph2 <- ggplot(accidents1, aes(x = Year, 
                 y = `Accident Total count`)) +
  geom_jitter(position = position_jitter(width = 0.5), aes(color = factor(Road.User)), size = 1.5, alpha = 5) +
  facet_wrap(Road.User~.) +
  theme(axis.text.x = element_text(size =10)) +
  theme_dark() +
  xlab("Year") +
  ylab("Total number of Accidents")

graph2

```


### Graph 3.3: Road user is more prone to accident with respect to state

```{r, eval=TRUE, echo=FALSE}
#How does the car accident fatality link with the district and accident type? 
accident_road_user_count <- fatalities %>% 
  select("State", "Road.User") %>% 
  filter(Road.User %in% c("Driver", "Motorcycle pillion passenger", "Motorcycle rider", "Passenger", "Pedal cyclist", "Pedestrian")) %>% 
  group_by(Road.User, State) %>% 
  count(Road.User) %>% 
  rename(`Accident Total count` = n)
```


```{r Graph3}
#plot the bar chart for the statistic of accident road user type in different states
graph3 <- ggplot(accident_road_user_count, 
       aes( x = State, y = `Accident Total count`, fill = Road.User)) + 
  geom_col(position = "dodge") +
  scale_fill_brewer( palette = "Set1" )

graph3
```